Considerations of Current and Emerging Transportation Management Center Data
This report examines the state of the practice and emerging trends in data, business models, and applications for transportation management centers (TMCs). It provides an understanding of what is available, how it is collected, the business models used by the companies that sell it, acceptable uses of the data, and possible data use cases. It also includes contract considerations for working with private sector data. This document further describes a process by which agencies can determine the potential value of internal data assets while also summarizing the private sector's desire for it.
The first section of this report serves as a reference for agencies who are looking to expand capabilities, and explains how emerging data sources will impact operations. Several new types of private sector data are discussed including connected vehicle data, realtime trajectory data, realtime turning movement data, high-resolution traffic signal data, and crowdsourced incident and congestion data. The private sector obtains its data largely from connected devices including cell phones, navigation systems, location-based services, satellites, and other devices embedded in vehicles. This collection strategy allows for wide-area coverage without the need to install dedicated road-side equipment. This ultimately reduces maintenance costs while simultaneously producing larger quantities of data, which is often of higher quality than that collected from legacy sensor-based systems. The private sector has yet to implement a way to produce ubiquitous realtime volume datasets, but progress is being made. The need for dedicated volume sensors will likely diminish over the next 5 years.
The data described in this report brings opportunities for new or improved transportation management capabilities, but there are trade-offs for each type of data. For example, crowdsourced data is susceptible to redundancy and unreliable information such as the generation of false-positive events, but it tends to provide broader coverage than individual agencies may be able to afford with limited resources and jurisdictional responsibilities. Crowdsourced data can also be timelier than agency-generated data.
This report also discusses how these emerging data sources may differ from traditional data sets already in use by TMCs. It provides a process by which agencies can go about determining the value of agency-owned data assets, and discusses the implications of monetizing the assets. The majority of the agencies interviewed for this report were unsuccessful at justifying cost recovery, shared revenue, and sponsorship models. Most private sector data providers still have a desire for agency-produced datasets; however, the type of data deemed most valuable has changed. Volume and speed sensor data from public agencies are no longer as valuable because of the growing penetration of probes. The private sector still values planned event and lane closure data, computer-aided dispatch (CAD) data, driving records or department of motor vehicle data, live closed-circuit television feeds, and parking availability.
Business Models and Procurement Strategies
Private sector data providers use a wide variety of business models and delivery mechanisms to provide data to government agencies. The differences between these models can have sweeping impacts on pricing, contracting, and agency use. Providers may price data by how the agency intends to use the data, such as for realtime use only (no archiving), archive only (historic data), and single-purpose use. The data may be sold for use only by that agency or shared with partner agencies. The agency may purchase all rights to use the data in perpetuity. Other business models include exchanging public sector for private sector data and potentially requiring the agency to promote the private sector's products and employ visible attribution.
When procuring data for use in TMCs, agencies should be prepared to address key policy considerations in selecting a data use agreement. The selection of usage terms by a public agency typically has long-term impacts on its capabilities and costs. These include:
When working with partner agencies to share internal data (like public safety CAD), the agency will need to focus their attention more on reintegration strategies that make the data more usable within the agency's existing advanced traffic management system platform, reduce duplication, reduce operator fatigue/overload, and ensure the agency staff (or systems) can properly interpret the information within the CAD messages.
Policy and Contract Considerations
The final sections of the report focus on the importance of negotiating acceptable use terms and conditions and understanding an agency's internal needs across different departments. Prior to the procurement process, agencies should communicate frequently across departments and with any relevant partners to understand the needs and potential future uses of the data. Sample data sharing agreements are provided for comparison. The report also talks about the pros and cons of leveraging the private sector for hosting and managing data as compared to developing internal data management capabilities. It is advisable to build reasonable and fair terms that determine what happens when or if data is deemed to be of poor quality, missing, etc.; on what performance metrics the data and contractor will be evaluated; and the implications of underperformance.
Success in reaching consensus on acceptable use terms and conditions is a result of a meaningful effort to understand agency internal needs across different departments, potential uses and interactions with partner agencies, private sector partners, academia, and the public. Prior to the procurement process, agencies should frequently communicate both internally and with any relevant partners to understand the needs and potential future uses of the data.
United States Department of Transportation - Federal Highway Administration